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Genetic and Environmental Prediction...

Cox, Jiayi Wu.

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Genetic and Environmental Prediction of Opioid Cessation Using Machine Learning, GWAS, and a Mouse Model.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Genetic and Environmental Prediction of Opioid Cessation Using Machine Learning, GWAS, and a Mouse Model./
作者:	Cox, Jiayi Wu.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	183 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-09, Section: B.
Contained By:	Dissertations Abstracts International81-09B.
標題:	Epidemiology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27665748
ISBN:	9781392383650

Genetic and Environmental Prediction of Opioid Cessation Using Machine Learning, GWAS, and a Mouse Model.
Cox, Jiayi Wu.

Genetic and Environmental Prediction of Opioid Cessation Using Machine Learning, GWAS, and a Mouse Model. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 183 p.

Source: Dissertations Abstracts International, Volume: 81-09, Section: B.

Thesis (Ph.D.)--Boston University, 2020.

This item must not be sold to any third party vendors.

The United States is currently experiencing an epidemic of opioid use, use disorder, and overdose-related deaths. While studies have identified several loci that are associated with opioid use disorder (OUD) risk, the genetic basis for the ability to discontinue opioid use has not been investigated. Furthermore, very few studies have investigated the non-genetic factors that are predictive of opioid cessation or their predictive ability.In this thesis, I studied a novel phenotype-opioid cessation, defined as the time since last use of illicit opioids ( 1 year ago as cease) among persons meeting lifetime DSM-5 criteria for opioid use disorder (OUD).In chapter two, I identified novel genetic variants and biological pathways that potentially regulate opioid cessation success through a genome wide study, as well as genetic overlap between opioid cessation and other substance cessation traits.In chapter three, I identified multiple non-genetic risk factors specific to each racial group that are predictive of opioid cessation from the same individuals analyzed in chapter two by applying several linear and non-linear machine learning techniques to a set of more than 3,000 variables assessed by a structured psychiatric interview. Factors identified from this atheoretical approach can be grouped into opioid use activities, other drug use, health conditions, and demographics, while the predictive accuracy as high as nearly 80% was achieved. The findings from this research generated more hypotheses for future studies to reference.In chapter four, I performed differential gene expression and network analysis on mice with different oxycodone (an opioid receptor agonist)-induced behaviors and compared the significantly associated genes and network modules with top-ranked genes identified in humans. The pathway cross-talks and gene homologs identified from both species illuminate the potential molecular mechanism of opioid behaviors.In summary, this thesis utilized statistical genetics, machine learning, and a computational biology framework to address factors that are associative with opioid cessation in humans, and cross-referenced the genetic findings in a mouse model. These findings serve as references for future studies and provide a framework for personalizing the treatment of OUD.

ISBN: 9781392383650Subjects--Topical Terms:

568544
Epidemiology.
Subjects--Index Terms:

Genetic factors

Genetic and Environmental Prediction of Opioid Cessation Using Machine Learning, GWAS, and a Mouse Model.
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520 $a The United States is currently experiencing an epidemic of opioid use, use disorder, and overdose-related deaths. While studies have identified several loci that are associated with opioid use disorder (OUD) risk, the genetic basis for the ability to discontinue opioid use has not been investigated. Furthermore, very few studies have investigated the non-genetic factors that are predictive of opioid cessation or their predictive ability.In this thesis, I studied a novel phenotype-opioid cessation, defined as the time since last use of illicit opioids ( 1 year ago as cease) among persons meeting lifetime DSM-5 criteria for opioid use disorder (OUD).In chapter two, I identified novel genetic variants and biological pathways that potentially regulate opioid cessation success through a genome wide study, as well as genetic overlap between opioid cessation and other substance cessation traits.In chapter three, I identified multiple non-genetic risk factors specific to each racial group that are predictive of opioid cessation from the same individuals analyzed in chapter two by applying several linear and non-linear machine learning techniques to a set of more than 3,000 variables assessed by a structured psychiatric interview. Factors identified from this atheoretical approach can be grouped into opioid use activities, other drug use, health conditions, and demographics, while the predictive accuracy as high as nearly 80% was achieved. The findings from this research generated more hypotheses for future studies to reference.In chapter four, I performed differential gene expression and network analysis on mice with different oxycodone (an opioid receptor agonist)-induced behaviors and compared the significantly associated genes and network modules with top-ranked genes identified in humans. The pathway cross-talks and gene homologs identified from both species illuminate the potential molecular mechanism of opioid behaviors.In summary, this thesis utilized statistical genetics, machine learning, and a computational biology framework to address factors that are associative with opioid cessation in humans, and cross-referenced the genetic findings in a mouse model. These findings serve as references for future studies and provide a framework for personalizing the treatment of OUD.
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